As businesses increasingly rely on technology to manage data related to operations, suitable systems for properly managing and validating data have become crucial to success. Particularly for large businesses, the amount of data utilized daily by businesses can be overwhelming. Accordingly, manual review and validation of such data is impractical, at best. In addition to normal sales data, businesses in countries where value-added taxes (VATs) are applied collect and utilize even more data, thereby raising additional potential points of failure.
The challenges facing customers seeking a refund and, in particular, seeking VAT refunds, may result in customers becoming discouraged and failing to follow through on obtaining their refunds. This issue is further compounded when the customer is an employee of an enterprise because the customer is not directly benefiting from the refund. Moreover, employees may submit irrelevant or duplicate information that is unnecessary for seeking refunds. Filtering through such unnecessary information may be time-consuming, costly, and subject to a large degree of human error.
Additionally, many existing solutions for validating based on data face challenges in obtaining the data required for validation. Specifically, existing solutions typically require either structured data or data that otherwise adheres to particular format requirements (e.g., a required size of a scanned image, markings indicating validation-related information, etc.) in order to identify and utilize the data. Such existing solutions face challenges when appropriately formatted data cannot be obtained. In particular, such existing solutions may be unable to complete validation or may return inaccurate results (e.g., false positives and false negatives) when required data cannot be properly identified. Such issues may occur when, for example, data is unstructured or semi-structured, or when data is at least partially structured but in an unrecognizable format so as to effectively render the data unstructured for analysis purposes. These issues are compounded when multiple sets of data requirements (e.g., requirements for VAT refunds and for RCM validation) must be met. Manual checking to account for these issues is inefficient and introduces new potential sources of error.
Further, when information used for validation is contained in, for example, receipts or other physical documents, electronic documents associated with that information typically include images. Such image-based electronic documents require more storage than text-based documents, and utilize more computing resources to transmit.
It would therefore be advantageous to provide a solution that would overcome the challenges noted above.